Spatial variation and predictors of composite index of HIV/AIDS knowledge, attitude and behaviours among Ethiopian women: A spatial and multilevel analyses of the 2016 Demographic Health Survey

Background Although the dissemination of health information is one of the pillars of HIV prevention efforts in Ethiopia, a large segment of women in the country still lack adequate HIV/AIDS knowledge, attitude, and behaviours. Despite many studies being conducted in Ethiopia, they mostly focus on the level of women’s knowledge about HIV/AIDS, failing to examine composite index of knowledge, attitude, and behaviour (KAB) domains comprehensively. In addition, the previous studies overlooked individual and community-level, and spatial predictors. Hence, this study aimed to estimate the prevalence, geographical variation (Hotspots), spatial predictors, and multilevel correlates of inadequate HIV/AIDS-Knowledge, Attitude, and Behaviour (HIV/AIDS-KAB) among Ethiopian women. Methods The study conducted using the 2016 Ethiopian Demographic and Health Survey data, included 12,672 women of reproductive age group (15–49 years). A stratified, two-stage cluster sampling technique was used; a random selection of enumeration areas (clusters) followed by selecting households per cluster. Composite index of HIV/AIDS-KAB was assessed using 11 items encompassing HIV/AIDS prevention, transmission, and misconceptions. Spatial analysis was carried out using Arc-GIS version 10.7 and SaTScan version 9.6 statistical software. Spatial autocorrelation (Moran’s I) was used to determine the non-randomness of the spatial variation in inadequate knowledge about HIV/AIDS. Multilevel multivariable logistic regression was performed, with the measure of association reported using adjusted odds ratio (AOR) with its corresponding 95% CI. Results The prevalence of inadequate HIV/AIDS-KAB among Ethiopian women was 48.9% (95% CI: 48.1, 49.8), with significant spatial variations across regions (global Moran’s I = 0.64, p<0.001). Ten most likely significant SaTScan clusters were identified with a high proportion of women with inadequate KAB. Somali and most parts of Afar regions were identified as hot spots for women with inadequate HIV/AIDS-KAB. Higher odds of inadequate HIV/AIDS-KAB was observed among women living in the poorest wealth quintile (AOR = 1.63; 95% CI: 1.21, 2.18), rural residents (AOR = 1.62; 95% CI: 1.18, 2.22), having no formal education (AOR = 2.66; 95% CI: 2.04, 3.48), non-autonomous (AOR = 1.71; 95% CI: (1.43, 2.28), never listen to radio (AOR = 1.56; 95% CI: (1.02, 2.39), never watched television (AOR = 1.50; 95% CI: 1.17, 1.92), not having a mobile phone (AOR = 1.45; 95% CI: 1.27, 1.88), and not visiting health facilities (AOR = 1.46; 95% CI: 1.28, 1.72). Conclusion The level of inadequate HIV/AIDS-KAB in Ethiopia was high, with significant spatial variation across regions, and Somali, and Afar regions contributed much to this high prevalence. Thus, the government should work on integrating HIV/AIDS education and prevention efforts with existing reproductive health services, regular monitoring and evaluation, and collaboration and partnership to tackle this gap. Stakeholders in the health sector should strengthen their efforts to provide tailored health education, and information campaigns with an emphasis on women who lack formal education, live in rural areas, and poorest wealth quintile should be key measures to enhancing knowledge. enhanced effort is needed to increase women’s autonomy to empower women to access HIV/AIDS information. The media agencies could prioritise the dissemination of culturally sensitive HIV/AIDS information to women of reproductive age. The identified hot spots with relatively poor knowledge of HIV/AIDS should be targeted during resource allocation and interventions.


Main documents
Material and Methods 4. The paper overlooks an essential aspect by failing to mention the data source and provide a direct link to the DHS portal site.Including this information is crucial for transparency and reproducibility, as it enables readers to access the original data for validation or further analysis.
Therefore, it is imperative to incorporate a clear statement specifying the data source and providing a hyperlink to the DHS portal site, ensuring that readers have easy access to the dataset used in the study.This addition will enhance the paper's credibility and facilitate future research endeavors by enabling others to replicate or build upon the findings.
5. The absence of a map depicting the study area is a notable omission in the paper.Including a map provides essential context for readers to visualize the geographical scope of the study and understand the spatial distribution of the variables under investigation.Incorporating a map showcasing the study area not only enhances the clarity of the research but also enables readers to better interpret the findings in relation to geographic features and boundaries.Therefore, it is recommended to include a detailed map illustrating the study area, its relevant geographical features, and any pertinent spatial data layers.This addition will enrich the paper by providing a visual representation of the research context, thereby enhancing its overall comprehensibility and impact.
6.The author's focus solely on community-level variables limited to region and residence overlooks other potentially significant factors such as community-level educational status, wealth status, media exposure, and others.These variables play crucial roles in shaping community dynamics and could significantly influence the outcomes under investigation.Therefore, it is essential to address this oversight and consider incorporating a broader range of community-level variables in the analysis.By including additional variables such as educational status, wealth status, and media exposure at the community level, the study can provide a more comprehensive understanding of the factors impacting the outcomes of interest.This expansion of variables will enrich the analysis and enhance the depth of insights derived from the research findings.
7. The absence of incremental autocorrelation analysis in the data analysis section is a notable oversight, particularly concerning autocorrelation analysis.Incremental autocorrelation analysis is crucial for understanding spatial dependence at varying distance thresholds, which can reveal important insights into the clustering patterns within the dataset.Without considering distance thresholds, the autocorrelation analysis may lack precision and potentially overlook significant spatial clustering trends.Therefore, it is imperative to address this deficiency and incorporate incremental autocorrelation analysis into the data analysis methodology 8.The Median Odds Ratio (MOR) in the Results section of the multilevel mixed-effect logistic regression analysis is not mention , especially considering its importance in understanding the variation between areas of highest and lowest risk.MOR provides valuable insights into the heterogeneity between different geographical areas, highlighting the extent to which individuallevel characteristics versus contextual factors contribute to the observed variations in risk.
Therefore, it is crucial to address this omission and include MOR analysis in the Results section.9.The discussion section appears to lack depth in its interpreting of the findings, particularly in integrating and comparing results from different analytical approaches such as hotspot analysis, SatScan, and others.Each of these methods provides unique insights into spatial patterns and clusters, and their integration can enrich the understanding of the phenomenon under study.
Therefore, it is essential to thoroughly discuss and contrast the findings obtained from each method, providing justification for any discrepancies observed.